BIONIX: A Wireless, Low-Cost Prosthetic Arm with Dual-Signal EEG and EMG Control
arXiv:2512.16929v1 Announce Type: new
Abstract: Affordable upper-limb prostheses often lack intuitive control systems, limiting functionality and accessibility for amputees in low-resource settings. This project presents a low-cost, dual-mode neuro-muscular control system integrating electroencepha...
QSMOTE-PGM/kPGM: QSMOTE Based PGM and kPGM for Imbalanced Dataset Classification
arXiv:2512.16960v1 Announce Type: new
Abstract: Quantum-inspired machine learning (QiML) leverages mathematical frameworks from quantum theory to enhance classical algorithms, with particular emphasis on inner product structures in high-dimensional feature spaces. Among the prominent approaches, th...
Compression is Routing: Reconstruction Error as an Intrinsic Signal for Modular Language Models
arXiv:2512.16963v1 Announce Type: new
Abstract: Current Large Language Models (LLMs) face three major challenges: context length limitations, high inference costs, and catastrophic forgetting during continual learning. While Mixture-of-Experts (MoE) architectures mitigate some of these conflicts, t...
Physics-Informed Lightweight Machine Learning for Aviation Visibility Nowcasting Across Multiple Climatic Regimes
arXiv:2512.16967v1 Announce Type: new
Abstract: Short-term prediction (nowcasting) of low-visibility and precipitation events is critical for aviation safety and operational efficiency. Current operational approaches rely on computationally intensive numerical weather prediction guidance and human-...
A new tool is revealing the invisible networks inside cancer
Spanish researchers have created a powerful new open-source tool that helps uncover the hidden genetic networks driving cancer. Called RNACOREX, the software can analyze thousands of molecular interactions at once, revealing how genes communicate inside tumors and how those signals relate to patient...
DiscoverDCP: A Data-Driven Approach for Construction of Disciplined Convex Programs via Symbolic Regression
arXiv:2512.15721v1 Announce Type: new
Abstract: We propose DiscoverDCP, a data-driven framework that integrates symbolic regression with the rule sets of Disciplined Convex Programming (DCP) to perform system identification. By enforcing that all discovered candidate model expressions adhere to DCP...
Hybrid Quantum-Classical Ensemble Learning for S\&P 500 Directional Prediction
arXiv:2512.15738v1 Announce Type: new
Abstract: Financial market prediction is a challenging application of machine learning, where even small improvements in directional accuracy can yield substantial value. Most models struggle to exceed 55--57\% accuracy due to high noise, non-stationarity, and ...
How Do Graph Signals Affect Recommendation: Unveiling the Mystery of Low and High-Frequency Graph Signals
arXiv:2512.15744v1 Announce Type: new
Abstract: Spectral graph neural networks (GNNs) are highly effective in modeling graph signals, with their success in recommendation often attributed to low-pass filtering. However, recent studies highlight the importance of high-frequency signals. The role of ...
LLaDA2.0: Scaling Up Diffusion Language Models to 100B
arXiv:2512.15745v1 Announce Type: new
Abstract: This paper presents LLaDA2.0 -- a tuple of discrete diffusion large language models (dLLM) scaling up to 100B total parameters through systematic conversion from auto-regressive (AR) models -- establishing a new paradigm for frontier-scale deployment....
BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design
We propose a general-purpose approach for improving the ability of Large Language Models (LLMs) to intelligently and adaptively gather information from a user or other external source using the framework of sequential Bayesian experimental design (BED). This enables LLMs to act as effective multi-tu...
LLM as a Neural Architect: Controlled Generation of Image Captioning Models Under Strict API Contracts
arXiv:2512.14706v1 Announce Type: new
Abstract: Neural architecture search (NAS) traditionally requires significant human expertise or automated trial-and-error to design deep learning models. We present NN-Caption, an LLM-guided neural architecture search pipeline that generates runnable image-cap...
Autonomous Source Knowledge Selection in Multi-Domain Adaptation
arXiv:2512.14710v1 Announce Type: new
Abstract: Unsupervised multi-domain adaptation plays a key role in transfer learning by leveraging acquired rich source information from multiple source domains to solve target task from an unlabeled target domain. However, multiple source domains often contain...
A Bayesian latent class reinforcement learning framework to capture adaptive, feedback-driven travel behaviour
arXiv:2512.14713v1 Announce Type: new
Abstract: Many travel decisions involve a degree of experience formation, where individuals learn their preferences over time. At the same time, there is extensive scope for heterogeneity across individual travellers, both in their underlying preferences and in...
AgREE: Agentic Reasoning for Knowledge Graph Completion on Emerging Entities
Open-domain Knowledge Graph Completion (KGC) faces significant challenges in an ever-changing world, especially when considering the continual emergence of new entities in daily news. Existing approaches for KGC mainly rely on pretrained language models’ parametric knowledge, pre-constructed queries...
The Communication Complexity of Distributed Estimation
We study an extension of the standard two-party communication model in which Alice and Bob hold probability distributions ppp and qqq over domains XXX and YYY, respectively. Their goal is to estimate
Ex∼p,y∼q[f(x,y)]\mathbb{E}_{x \sim p, y \sim q}[f(x, y)]Ex∼p,y∼q[f(x,y)]
to within additive error ε...
A decision-theoretic characterization of perfect calibration is that an agent seeking to minimize a proper loss in expectation cannot improve their outcome by post-processing a perfectly calibrated predictor. Hu and Wu (FOCS’24) use this to define an approximate calibration measure called calibratio...
Unified Open-World Segmentation with Multi-Modal Prompts
Recent years have witnessed the rapid development of open-world image segmentation, including open-vocabulary segmentation and in-context segmentation. Nonetheless, existing methods are limited to a single modality prompt, which lacks the flexibility and accuracy needed for complex object-aware prom...
The Model Context Protocol (MCP) is genuinely useful. It gives people who develop AI tools a standardized way to call functions and access data from external systems. Instead of building custom integrations for each data source, you can expose databases, APIs, and internal tools through a common pro...
Most-Read: The Stanford HAI Stories that Defined AI in 2025
Readers wanted to know if their therapy chatbot could be trusted, whether their boss was automating the wrong job, and if their private conversations were training tomorrow's models.